Nursing ethics for prevention and control of major infectious disease outbreaks: Chinese expert consensus

A country’s ability to prepare for, detect, and respond to infectious disease outbreaks such as the Ebola outbreaks in West Africa and the Democratic Republic of the Congo, and the recent Zika outbreak in the Latin American and Caribbean region, depends greatly on its competency in mobilizing skilled staff, and in providing and resupplying its health system with essential infection prevention and control commodities during public health emergencies. Health systems in most developing countries suffer from fragmentation; limited coordination and leadership; lack of national health security policies and legislation; low staff capacity and competency; and inadequate information systems necessary for decision making. The global health security agenda (GHSA) has stepped up efforts to build the capacity of such countries to effectively respond to health emergencies by strengthening health systems preparedness and ability to respond to outbreaks and epidemics. Chemonics International leads the implementation of multiple efforts to strengthen health systems across the world in support of the GHSA mission, both through the implementation of USAID funded activities and through its membership in the GHSA Private Sector Roundtable. Under the USAID Global Health Supply Chain—Technical Assistance Francophone Task Order, Chemonics developed a framework of essential competencies for emergency supply chain (ESC) management to help countries prepare and respond to outbreaks and epidemics. This framework has been piloted in Cameroon, and has wide-ranging applications for ministries of health, central medical stores, and regional level logistics units. Additionally, Chemonics, through the USAID funded Human Resources for Health in 2030 (HRH2030) program, supports the efforts of National One Health Platforms to ensure efficient multisectoral coordination; support integrated surveillance, preparedness, and response systems; and develop emerging diseases preparedness and response plans as well as prevention and control strategies, including leading the first ever simulation exercise in Ethiopia focused on Highly Pathogenic Avian Influenza.

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Environmental scan of infection prevention and control practices for containment of hospital-acquired infectious disease outbreaks in acute care hospital settings across Canada

American Journal of Infection Control ◽

10.1016/j.ajic.2017.05.014 ◽

2017 ◽

Vol 45 (10) ◽

pp. 1116-1126 ◽

Cited By ~ 5

Author(s):

Wrechelle Ocampo ◽

Rose Geransar ◽

Nancy Clayden ◽

Jessica Jones ◽

Jill de Grood ◽

...

Keyword(s):

Infectious Disease ◽

Prevention And Control ◽

Infection Prevention ◽

Acute Care Hospital ◽

Disease Outbreaks ◽

Infection Prevention And Control ◽

Care Hospital ◽

Infectious Disease Outbreaks ◽

Hospital Acquired ◽

And Control

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Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany

Eurosurveillance ◽

10.2807/1560-7917.es.2017.22.26.30562 ◽

2017 ◽

Vol 22 (26) ◽

Cited By ~ 4

Author(s):

Loes Soetens ◽

Susan Hahné ◽

Jacco Wallinga

Keyword(s):

Infectious Disease ◽

The Netherlands ◽

Q Fever ◽

Disease Outbreaks ◽

Point Pattern ◽

Infectious Disease Outbreaks ◽

Public Health Professionals ◽

Potential Source ◽

High Incidence ◽

And Control

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

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Optimizing spatial and seasonal deployment of vaccination campaigns to eliminate wildlife rabies

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0280 ◽

2019 ◽

Vol 374 (1776) ◽

pp. 20180280 ◽

Cited By ~ 7

Author(s):

Laurie Baker ◽

Jason Matthiopoulos ◽

Thomas Müller ◽

Conrad Freuling ◽

Katie Hampson

Keyword(s):

Infectious Disease ◽

Plant Pathogens ◽

Herd Immunity ◽

Disease Outbreaks ◽

Vaccination Strategy ◽

Theme Issue ◽

Infectious Disease Outbreaks ◽

Control Programmes ◽

The Face ◽

And Control

Understanding how the spatial deployment of interventions affects elimination time horizons and potential for disease re-emergence has broad application to control programmes targeting human, animal and plant pathogens. We previously developed an epidemiological model that captures the main features of rabies spread and the impacts of vaccination based on detailed records of fox rabies in eastern Germany during the implementation of an oral rabies vaccination (ORV) programme. Here, we use simulations from this fitted model to determine the best vaccination strategy, in terms of spatial placement and timing of ORV efforts, for three epidemiological scenarios representative of current situations in Europe. We found that consecutive and comprehensive twice-yearly vaccinations across all regions rapidly controlled and eliminated rabies and that the autumn campaigns had the greater impact on increasing the probability of elimination. This appears to result from the need to maintain sufficient herd immunity in the face of large birth pulses, as autumn vaccinations reach susceptible juveniles and therefore a larger proportion of the population than spring vaccinations. Incomplete vaccination compromised time to elimination requiring the same or more vaccination effort to meet similar timelines. Our results have important practical implications that could inform policies for rabies containment and elimination in Europe and elsewhere. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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Perfect counterfactuals for epidemic simulations

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0279 ◽

2019 ◽

Vol 374 (1776) ◽

pp. 20180279 ◽

Cited By ~ 4

Author(s):

Joshua Kaminsky ◽

Lindsay T. Keegan ◽

C. Jessica E. Metcalf ◽

Justin Lessler

Keyword(s):

Infectious Disease ◽

Disease Outbreaks ◽

Computation Time ◽

Control Measures ◽

Mathematical Representation ◽

Theme Issue ◽

Stochastic Variation ◽

Infectious Disease Outbreaks ◽

Negative Effect ◽

And Control

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervention, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a ‘single-world’ approach to matching simulations of controlled epidemics to their exact uncontrolled counterfactual. Our method borrows concepts from percolation approaches, prunes out possible epidemic histories and creates potential epidemic graphs (i.e. a mathematical representation of all consistent epidemics) that can be ‘realized’ to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models (e.g. SIR models), and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids nonsensical inferences. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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Increased frequency of travel in the presence of cross-immunity may act to decrease the chance of a global pandemic

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0274 ◽

2019 ◽

Vol 374 (1775) ◽

pp. 20180274 ◽

Cited By ~ 13

Author(s):

R. N. Thompson ◽

C. P. Thompson ◽

O. Pelerman ◽

S. Gupta ◽

U. Obolski

Keyword(s):

Infectious Disease ◽

Influenza Pandemic ◽

Disease Outbreaks ◽

Theme Issue ◽

Infectious Disease Outbreaks ◽

Global Pandemic ◽

Cross Immunity ◽

1918 Influenza ◽

And Control ◽

Epidemic Forecasting

The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains—a low virulence (LV) strain outbreak followed by a high virulence (HV) strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the HV strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the HV strain, driven by exposure to the LV strain. As a result, major epidemics of the HV strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

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Critical transitions in malaria transmission models are consistently generated by superinfection

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0275 ◽

2019 ◽

Vol 374 (1775) ◽

pp. 20180275 ◽

Cited By ~ 4

Author(s):

David Alonso ◽

Andy Dobson ◽

Mercedes Pascual

Keyword(s):

Infectious Disease ◽

Disease Outbreaks ◽

Distribution Patterns ◽

Past Century ◽

Theme Issue ◽

Vector Borne Diseases ◽

Infectious Disease Outbreaks ◽

Critical Transitions ◽

Vector Borne ◽

And Control

The history of modelling vector-borne infections essentially begins with the papers by Ross on malaria. His models assume that the dynamics of malaria can most simply be characterized by two equations that describe the prevalence of malaria in the human and mosquito hosts. This structure has formed the central core of models for malaria and most other vector-borne diseases for the past century, with additions acknowledging important aetiological details. We partially add to this tradition by describing a malaria model that provides for vital dynamics in the vector and the possibility of super-infection in the human host: reinfection of asymptomatic hosts before they have cleared a prior infection. These key features of malaria aetiology create the potential for break points in the prevalence of infected hosts, sudden transitions that seem to characterize malaria’s response to control in different locations. We show that this potential for critical transitions is a general and underappreciated feature of any model for vector-borne diseases with incomplete immunity, including the canonical Ross–McDonald model. Ignoring these details of the host’s immune response to infection can potentially lead to serious misunderstanding in the interpretation of malaria distribution patterns and the design of control schemes for other vector-borne diseases.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

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The Role of Animal Research in Pandemic Responses

Comparative Medicine ◽

10.30802/aalas-cm-21-000062 ◽

2021 ◽

Author(s):

Jacqueline K Brockhurst ◽

Jason S Villano

Keyword(s):

Infectious Disease ◽

Disease Outbreaks ◽

Animal Research ◽

Infectious Disease Outbreaks ◽

Treatment And Prevention ◽

Infectious Disease Research ◽

Spanish Flu ◽

Epidemic Response ◽

Major Infectious Disease

The significant advances made by the global scientific community during the COVID-19 pandemic, exemplified by thedevelopment of multiple SARS-CoV-2 vaccines in less than 1 y, were made possible in part because of animal research.Historically, animals have been used to study the characterization, treatment, and prevention of most of the major infectious disease outbreaks that humans have faced. From the advent of modern ‘germ theory’ prior to the 1918 Spanish Flu pandemic through the more recent Ebola and Zika virus outbreaks, research that uses animals has revealed or supported key discoveries in disease pathogenesis and therapy development, helping to save lives during crises. Here we summarize the role of animal research in past pandemic and epidemic response efforts, as well as current and future considerations for animal research in the context of infectious disease research.

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Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2018.0264 ◽

2019 ◽

Vol 374 (1776) ◽

pp. 20180264 ◽

Cited By ~ 11

Author(s):

G. L. Chaters ◽

P. C. D. Johnson ◽

S. Cleaveland ◽

J. Crispell ◽

W. A. de Glanville ◽

...

Keyword(s):

Infectious Disease ◽

Disease Control ◽

Disease Transmission ◽

Disease Outbreaks ◽

Routine Data ◽

Contact Layer ◽

Theme Issue ◽

Targeted Interventions ◽

Infectious Disease Outbreaks ◽

And Control

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ ( R 0 = 3) and ‘slow’ ( R 0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

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